We survey the adversarial robustness of neural networks from the perspective of Lipschitz
calculus in a unifying fashion by expressing models, attacks and safety guarantees, that is, a …

Chordal and factor-width decomposition methods for semidefinite programming and
polynomial optimization have recently enabled the analysis and control of large-scale linear …

Designing neural networks with bounded Lipschitz constant is a promising way to obtain
certifiably robust classifiers against adversarial examples. However, the relevant progress …

Numerous deep learning algorithms have been inspired by and understood via the notion of
information bottleneck, where unnecessary information is (often implicitly) minimized while …

Due to their susceptibility to adversarial perturbations, neural networks (NNs) are hardly
used in safety-critical applications. One measure of robustness to such perturbations in the …

Lipschitz constants are connected to many properties of neural networks, such as
robustness, fairness, and generalization. Existing methods for computing Lipschitz constants …

As an emerging technique, mobile-edge computing (MEC) introduces a new scheme for
various distributed communication-computing systems, such as industrial Internet of Things …

We introduce a new paradigm for solving regularized variational problems. These are
typically formulated to address ill-posed inverse problems encountered in signal and image …

Lipschitz constants of neural networks have been explored in various contexts in deep
learning, such as provable adversarial robustness, estimating Wasserstein distance …

Recent studies show that training deep neural networks (DNNs) with Lipschitz constraints
are able to enhance adversarial robustness and other model properties such as stability. In …